Real-Time In-Cabin Driver Behavior Recognition on Low-Cost Edge Hardware
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909983067602944 |
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| author | Ahsani, Vesal Khalaj, Babak Hossein Shah-Mansouri, Hamed |
| author_facet | Ahsani, Vesal Khalaj, Babak Hossein Shah-Mansouri, Hamed |
| contents | In-cabin driver monitoring systems (DMS) must recognize distraction- and drowsiness-related behaviors with low latency under strict constraints on compute, power, and cost. We present a single-camera in-cabin driver behavior recognition system designed for deployment on two low-cost edge platforms: Raspberry Pi 5 (CPU-only) and the Google Coral development board with an Edge Tensor Processing Unit (Edge TPU) accelerator. The proposed pipeline combines (i) a compact per-frame vision model, (ii) a confounder-aware label taxonomy to reduce confusions among visually similar behaviors, and (iii) a temporal decision head that triggers alerts only when predictions are both confident and sustained. The system supports 17 behavior classes. Training and evaluation use licensed datasets plus in-house collection (over 800,000 labeled frames) with driver-disjoint splits, and we further validate the deployed system in live in-vehicle tests. End-to-end performance reaches approximately 16 FPS on Raspberry Pi 5 using 8-bit integer (INT8) inference (per-frame latency <60 ms) and approximately 25 FPS on Coral Edge TPU (end-to-end latency ~40 ms), enabling real-time monitoring and stable alert generation on embedded hardware. Finally, we discuss how reliable in-cabin perception can serve as an upstream signal for human-centered vehicle intelligence, including emerging agentic vehicle concepts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time In-Cabin Driver Behavior Recognition on Low-Cost Edge Hardware Ahsani, Vesal Khalaj, Babak Hossein Shah-Mansouri, Hamed Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning Image and Video Processing In-cabin driver monitoring systems (DMS) must recognize distraction- and drowsiness-related behaviors with low latency under strict constraints on compute, power, and cost. We present a single-camera in-cabin driver behavior recognition system designed for deployment on two low-cost edge platforms: Raspberry Pi 5 (CPU-only) and the Google Coral development board with an Edge Tensor Processing Unit (Edge TPU) accelerator. The proposed pipeline combines (i) a compact per-frame vision model, (ii) a confounder-aware label taxonomy to reduce confusions among visually similar behaviors, and (iii) a temporal decision head that triggers alerts only when predictions are both confident and sustained. The system supports 17 behavior classes. Training and evaluation use licensed datasets plus in-house collection (over 800,000 labeled frames) with driver-disjoint splits, and we further validate the deployed system in live in-vehicle tests. End-to-end performance reaches approximately 16 FPS on Raspberry Pi 5 using 8-bit integer (INT8) inference (per-frame latency <60 ms) and approximately 25 FPS on Coral Edge TPU (end-to-end latency ~40 ms), enabling real-time monitoring and stable alert generation on embedded hardware. Finally, we discuss how reliable in-cabin perception can serve as an upstream signal for human-centered vehicle intelligence, including emerging agentic vehicle concepts. |
| title | Real-Time In-Cabin Driver Behavior Recognition on Low-Cost Edge Hardware |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2512.22298 |